Kipu Quantum Launches Rimay Quantum Feature Extraction in IBM Quantum Functions Catalog

Quantum software developer Kipu Quantum has made its Rimay quantum feature extraction service available in the IBM Quantum Functions Catalog, giving IBM Quantum Flex, Premium, and On-Prem Plan users direct API access to quantum-enhanced machine learning within existing data science pipelines. Following the earlier catalog release of Kipu’s Iskay Quantum Optimizer, Rimay expands the company’s commercial presence on IBM Quantum hardware from combinatorial optimization into tabular and multimodal feature engineering.

Rather than replacing established machine learning models, Rimay operates as a drop-in preprocessing layer between train-test data splitting and classical model training. During the initial fit-transform phase, the function maps normalized numerical features to individual qubits and encodes pairwise mutual information into Hamiltonian coupling terms. After executing digitized counterdiabatic circuits on the quantum processor, it returns an enriched CSV matrix that teams can feed directly into standard frameworks such as scikit-learn, XGBoost, CatBoost, or LightGBM.

To support repeatable production inference, Rimay stores the trained Hamiltonian topology under a persistent mapping_id bound to the calibrated processor layout. Data science teams can subsequently invoke transform mode on new, unlabeled datasets without re-fitting the feature map, or pair the service with Kipu’s offline classical surrogate framework to eliminate quantum hardware dependencies during high-volume inference.